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One Approach of Approximation for Incoming Data Stream in IoT Based Monitoring System

机译:基于物联网监控系统中传入数据流近似的一种方法

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IoT devices and platforms are a fast growing market. One can mention a number of businesses relying on easy opportunity to build real-time monitoring systems using modern software and IoT hardware solutions. However, the growth has revealed a number of complex problems. Many problems are in area of data processing and storing huge volumes of information. Due to wide use of different kinds of sensors, and even a sets of sensors within each single device, on one hand, practitioners discover unpleasant effects of data losses caused by data packages losses or delays while its transition from sensor to server. On the other hand, huge volumes of data require to use some big data approaches and many startup projects feel the problem of lack of resources. Many of them feel lack of data storage facilities or become unable to support huge data sets due to lack of finance. The paper is focused to research the problem approximation for incoming data stream to make it smaller the volume of data to be stored but to keep it possible to be used. A few approaches to use such data compression via its approximation are discussed with application to IoT based real-time monitoring system.
机译:物联网设备和平台是一个快速增长的市场。人们可以提到许多企业依赖于轻松的机会使用现代软件和IoT硬件解决方案构建实时监控系统。然而,增长揭示了许多复杂问题。许多问题都在数据处理领域和存储巨大的信息。由于各种传感器的广泛使用,甚至在每个设备内部的一组传感器,一方面,从业者发现数据丢失引起的数据丢失造成的令人不快的效果,而其从传感器转换到服务器。另一方面,大量的数据需要使用一些大数据方法,并且许多启动项目感受到缺乏资源的问题。其中许多人缺乏数据存储设施,或者由于缺乏金融而无法支持巨大的数据集。本文的重点是研究输入数据流的问题近似,以使其更少的要存储的数据量,而是可以使用。通过应用于基于IOT的实时监控系统,讨论了通过其近似使用这种数据压缩的几种方法。

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